Only Balouchzahi et al. (2022) [1]} presented a model using sequential neural networks that were trained on Linguistic Inquiry and Word Count (LIWC) psychological features [2]} combined with n-gram features. They scored the third rank with averaged-weighted scores of 0.87 and 0.79 for English and Spanish texts, respectively. Also, one of the teams [3]} used the over-sampling technique to address the imbalanced dataset and used a 1D CNN-LSTM architecture to tackle the hope speech detection in English comments. Their methodology proved its effectiveness by securing the first rank in the competition. A summary of various techniques used for binary hope speech detection in English is presented in Table REF .
